### Chapter:
The Realm of Supervised Learning

####
Preprocessing Data Using Different Techniques

06m 38s

####
Building a Linear Regressor

04m 25s

####
Regression Accuracy and Model Persistence

03m 41s

####
Building a Ridge Regressor

02m 41s

####
Building a Polynomial Regressor

02m 33s

####
Estimating housing prices

03m 45s

####
Computing relative importance of features

01m 54s

####
Estimating bicycle demand distribution

04m 35s

### Chapter:
Constructing a Classifier

####
Building a Simple Classifier

03m 40s

####
Building a Logistic Regression Classifier

04m 50s

####
Building a Naive Bayes’ Classifier

02m 11s

####
Splitting the Dataset for Training and Testing

01m 23s

####
Evaluating the Accuracy Using Cross-Validation

04m 6s

####
Visualizing the Confusion Matrix and Extracting the Performance Report

04m 14s

####
Evaluating Cars based on Their Characteristics

05m 12s

####
Extracting Validation Curves

02m 49s

####
Extracting Learning Curves

01m 37s

####
Extracting the Income Bracket

03m 36s

### Chapter:
Predictive Modeling

####
Building a Linear Classifier Using Support Vector Machine

04m 23s

####
Building Nonlinear Classifier Using SVMs

01m 47s

####
Tackling Class Imbalance

02m 53s

####
Extracting Confidence Measurements

02m 36s

####
Finding Optimal Hyper-Parameters

02m 16s

####
Building an Event Predictor

03m 45s

####
Estimating Traffic

02m 39s

### Chapter:
Clustering with Unsupervised Learning

####
Clustering Data Using the k-means Algorithm

03m 7s

####
Compressing an Image Using Vector Quantization

03m 37s

####
Building a Mean Shift Clustering

02m 35s

####
Grouping Data Using Agglomerative Clustering

03m 4s

####
Evaluating the Performance of Clustering Algorithms

02m 55s

####
Automatically Estimating the Number of Clusters Using DBSCAN

03m 34s

####
Finding Patterns in Stock Market Data

02m 34s

####
Building a Customer Segmentation Model

02m 21s

### Chapter:
Building Recommendation Engines

####
Building Function Composition for Data Processing

03m 25s

####
Building Machine Learning Pipelines

03m 54s

####
Finding the Nearest Neighbors

01m 56s

####
Constructing a k-nearest Neighbors Classifier

04m 18s

####
Constructing a k-nearest Neighbors Regressor

02m 43s

####
Computing the Euclidean Distance Score

02m 8s

####
Computing the Pearson Correlation Score

01m 55s

####
Finding Similar Users in a Dataset

01m 35s

####
Generating Movie Recommendations

02m 34s

### Chapter:
Analyzing Text Data

####
Preprocessing Data Using Tokenization

03m 0s

####
Stemming Text Data

02m 22s

####
Converting Text to Its Base Form Using Lemmatization

02m 11s

####
Dividing Text Using Chunking

02m 3s

####
Building a Bag-of-Words Model

02m 58s

####
Building a Text Classifier

04m 43s

####
Identifying the Gender

02m 17s

####
Analyzing the Sentiment of a Sentence

03m 9s

####
Identifying Patterns in Text Using Topic Modelling

04m 52s

### Chapter:
Speech Recognition

####
Reading and Plotting Audio Data

02m 34s

####
Transforming Audio Signals into the Frequency Domain

02m 9s

####
Generating Audio Signals with Custom Parameters

01m 45s

####
Synthesizing Music

02m 10s

####
Extracting Frequency Domain Features

02m 6s

####
Building Hidden Markov Models

02m 19s

####
Building a Speech Recognizer

03m 12s

### Chapter:
Dissecting Time Series and Sequential Data

####
Transforming Data into the Time Series Format

03m 7s

####
Slicing Time Series Data

01m 31s

####
Operating on Time Series Data

01m 42s

####
Extracting Statistics from Time Series

02m 29s

####
Building Hidden Markov Models for Sequential Data

04m 15s

####
Building Conditional Random Fields for Sequential Text Data

04m 27s

####
Analyzing Stock Market Data with Hidden Markov Models

02m 25s

### Chapter:
Image Content Analysis

####
Operating on Images Using OpenCV-Python

03m 7s

####
Histogram Equalization

02m 30s

####
Detecting Corners and SIFT Feature Points

03m 46s

####
Building a Star Feature Detector

01m 34s

####
Creating Features Using Visual Codebook and Vector Quantization

04m 10s

####
Training an Image Classifier Using Extremely Random Forests

02m 30s

####
Building an object recognizer

01m 53s

### Chapter:
Biometric Face Recognition

####
Capturing and Processing Video from a Webcam

01m 58s

####
Building a Face Detector using Haar Cascades

02m 40s

####
Building Eye and Nose Detectors

01m 54s

####
Performing Principal Component Analysis

02m 17s

####
Performing Kernel Principal Component Analysis

02m 2s

####
Performing Blind Source Separation

02m 16s

####
Building a Face Recognizer Using a Local Binary Patterns Histogram

04m 14s

### Chapter:
Deep Neural Networks

####
Building a Perceptron

02m 40s

####
Building a Single-Layer Neural Network

01m 37s

####
Building a deep neural network

02m 19s

####
Creating a Vector Quantizer

01m 40s

####
Building a Recurrent Neural Network for Sequential Data Analysis

02m 23s

####
Visualizing the Characters in an Optical Character Recognition Database

01m 48s

####
Building an Optical Character Recognizer Using Neural Networks

02m 28s

### Chapter:
Visualizing Data

####
Plotting 3D Scatter plots

02m 42s

####
Plotting Bubble Plots

01m 16s

####
Animating Bubble Plots

01m 56s

####
Drawing Pie Charts

01m 33s

####
Plotting Date-Formatted Time Series Data

01m 33s

####
Plotting Histograms

01m 5s

####
Visualizing Heat Maps

01m 15s

####
Animating Dynamic Signals

02m 6s